Delayed telop aid

ABSTRACT

The proposed system, Delayed Telop Aid (DTA), improves the teleoperator&#39;s ability to control the vehicle in a three step process. First, DTA predicts robot motion given the operators commands. Second, DTA creates synthetic images to produce a video feed that looks as if the robot communication link had no delay and no reduced bandwidth. Finally, DTA performs closed loop control on the robot platform to ensure that the robot follows the operator&#39;s commands. A closed loop control of the platform makes sure that the predicted pose after the delay (and therefore the image presented to the operator) is achieved by the platform. This abstracts away the latency-sensitive parts of the robot control, making the robot&#39;s behavior stable in the presence of poorly characterized latency between the operator and the vehicle.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from and is a continuation of U.S.patent application Ser. No. 14/219,834, entitled “Delayed Telop Aid”,filed on 19 Mar. 2014. The benefit under 35 USC § 119(e) of the UnitedStates provisional application is hereby claimed, and the aforementionedapplication is hereby incorporated herein by reference.

This application claims priority from U.S. Patent Application Ser. No.61/803,440, entitled “Delayed Telop Aid”, filed on 19 Mar. 2013. Thebenefit under 35 USC § 119(e) of the United States provisionalapplication is hereby claimed, and the aforementioned application ishereby incorporated herein by reference.

FEDERALLY SPONSORED RESEARCH

Not Applicable

SEQUENCE LISTING OR PROGRAM

Not Applicable

TECHNICAL FIELD OF THE INVENTION

This invention relates to a teleoperator's ability to control a remotecontrolled, unmanned vehicle In particular, the invention relates to aclosed loop control of a remote controlled, unmanned vehicle ensuringthat a predicted pose after the delay (and therefore the image presentedto the operator) is generated and displayed by the platform.

BACKGROUND OF THE INVENTION

Most people who have teleoperated a robotic system easily recognize thechallenges imposed by the reduced situational awareness, and the effectsof delays and poor communications. Control of SUGVs in clutteredenvironments requires fine control of the platform to avoid obstacles,and align the platforms with doorways or stairways. On larger UGVs,speed is normally paramount for mission success, and communicationdelays can cause control instabilities that often result in overturnedor damaged vehicles.

Recent developments in UGVs have led to significant increases inmilitary spending. It is estimated that global spending on UGVs reached$418 million in 2010 [12]. As the capabilities of these UGVs areexpanded past Explosive Ordnance Disposal (EOD) and into perimetersurveillance, logistics support and armed combat, the market is expectedto grow to upwards of $2.9 billion by 2016. This growth will primarilybe driven by military, homeland security and law enforcement sectors. Inparticular, the U.S. military will be a significant engine of growth dueto a Congressionally Directed Goal of ⅓ of ground combat vehicles shouldbe unmanned by 2015.

With bombs, mines and improvised explosive devices (IEDs) littering thebattlefield, soldier's lives are put at extremely high risk during bombdiffusion missions. Militaries have sought many ways to reduce theexposure of personnel to such hazardous conditions. Robots offer theperfect solution. Extensive research is being done on teleoperation andways to remotely control the aforementioned robots. For these robots tobe used effectively, information from sensors, microphones and camerasmust be relayed in real time or create a “virtual real time” experience.Extreme precision and complete situational awareness is needed whendetecting and disarming explosives.

The ongoing battle in the Middle East has caused a spike in interest inthese robotic soldiers. According to Lieutenant General Michael Oates,in 2011 “IEDs are still responsible for the greatest number of ourcasualties in Iraq and Afghanistan”. Such danger has been reduceddrastically, 37 percent, thanks in part to the militaries increased useof drones to detect IEDs. Still, however, insurgents show no sign ofslowing, as the number of IEDs planted are between 1,300 and 1,500 permonth, increasing the need for more EOD robots to be deployed.

Intelligence gathering has been a staple of warfare since the beginningof organized combat, and without which any army is bound to fail. Often,these missions leave soldiers exposed and vulnerable to attacks whensurveying, especially behind enemy lines. Modern warfare has allowed theInventors to utilize unmanned vehicles to carry out such missions.Replacing soldiers with unmanned vehicles not only allows for more indepth surveillance, but also saves the lives of those soldiers sent onreconnaissance missions. Further, robots can operate for hours withoutfatigue and loss of perception unlike humans, increasing theireffectiveness at evaluating enemy positions and territory. In 2001, themilitary had commissioned only 120 teleoperated robots for use in theMiddle Eastern. As of 2008, however, ground robots had increased to morethan 6,000 in theater.

This massive spike in interest and demand for unmanned vehicles hasincreased the need for an improved and reliable visual processingalgorithm to reduce, or even completely remove, data transmission lagbetween robot and operator. Control of SUGVs in cluttered environmentsrequires fine control of the platform to avoid obstacles, and align theplatforms with doorways or stairways. On larger UGVs, speed is normallyparamount for mission success, and communication delays can causecontrol instabilities that often result in overturned or damagedvehicles. In order to improve the teleoperator's ability to control therobot, algorithms that create future synthetic images, and predictplatform poses are used to create an image on the Operator Control Unitscreen that smoothes out the stops/starts/jumps and irregular videofeed. Currently, operators must be in the line of sight of the robot,otherwise serious data transmission lag can occur, rendering themineffective. Additionally, this lag can cause cognitive fatigue in theoperator, causing headaches and stress. These time delays can causeoperations to go awry. The Darkstar UAV had a seven (7) second delaybetween remote command and implementation. The operators were not ableto send commands to the Darkstar in time, resulting in an unpredictablecrash during take-off. Such events have sparked significant demand forpredictive displays to eliminate lag, cognitive fatigue, and increaseremote operation distances.

Definitions

AUGV is defined as an Autonomous Unmanned Ground Vehicle.

DTA is defined as Delayed Telop Aid.

OCU is defined as Operator Control Unit.

“Platform Pose” is defined as the combined location and orientation ofthe platform of the robot or vehicle.

SIFT is defined as Scale Invariant Feature Transform.

SURF is defined as Speeded Up Robust Features.

LADAR (LAser Detection And Ranging) systems use light to determine thedistance to an object.

UGV is defined as an Unmanned Ground Vehicle.

SUMMARY OF THE INVENTION

The proposed system, Delayed Telop Aid (DTA), improves theteleoperator's ability to control the vehicle in a three step process.First, DTA predicts robot motion given the operators commands. Second,DTA creates synthetic images to produce a video feed that looks as ifthe robot communication link had no delay and no reduced bandwidth.Finally, DTA performs closed loop control on the robot platform toensure that the robot follows the operator's commands.

This final step is one of the main characteristics that sets DTA apartfrom other approaches, a closed loop control of the platform makes surethat the predicted pose after the delay (and therefore the imagepresented to the operator) is achieved by the platform. This abstractsaway the latency-sensitive parts of the robot control, making therobot's behavior stable in the presence of poorly characterized latencybetween the operator and the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate the present invention and, togetherwith the description, further serve to explain the principles of theinvention and to enable a person skilled in the pertinent art to makeand use the invention.

FIG. 1 shows the DTA system architecture;

FIGS. 2A-C illustrates a live teleoperation exercise using DTA;

FIG. 3 illustrates a SUGV test platform;

FIG. 4 illustrates SURF feature tracking between consecutive images;

FIG. 5 illustrates tracking and prediction using navigation data;

FIG. 6 illustrates tracking and prediction without using navigationdata;

FIG. 7 shown an original image with tracks in red and predicted featurelocations in blue and a reconstructed warped image using a Delaunay meshusing predicted feature locations;

FIG. 8 illustrates a synthetic image at predicted time T+1 (Right) and areal image taken at time T+1;

FIG. 9a illustrates Delaunay Mesh Generation vs. Thin plate SplineInterpolation;

FIG. 9b illustrates the Delaunay Mesh Generation;

FIG. 9c illustrates the original image with tracked SURF features;

FIG. 10a illustrates an attempt to fit a quadratic equation function tothe depth values of tracked features in an effort to draw the surface inOpenGL using the depths derived from this fitted function rather thandrawing a flat wall at an arbitrary depth as used in a more simpleOpenGL based approach;

FIG. 10b illustrates and example of reconstruction using curve fittingwhere the original image is shown with tracked features;

FIG. 10c illustrates a synthetic image where the compressed image istextured mapped to a surface that matches the curve shown in FIG. 10 a;

FIG. 11A illustrates a sensor system designed and implemented on amilitary-relevant small robotic platform;

FIG. 11a illustrates the small robotic platform.

FIG. 11b illustrates 3-D scans of an office space using the smallrobotic platform shown in FIG. 11 a;

FIG. 12 illustrates the Sensor Data Capture and Processing Module on theplatform extracts 3D feature locations from the sensor selected by theuser; and

FIGS. 13a-c show two approaches to displaying the 3D environment.

DESCRIPTION OF THE INVENTION

In the following detailed description of the invention of exemplaryembodiments of the invention, reference is made to the accompanyingdrawings (where like numbers represent like elements), which form a parthereof, and in which is shown by way of illustration specific exemplaryembodiments in which the invention may be practiced. These embodimentsare described in sufficient detail to enable those skilled in the art topractice the invention, but other embodiments may be utilized andlogical, mechanical, electrical, and other changes may be made withoutdeparting from the scope of the present invention. The followingdetailed description is, therefore, not to be taken in a limiting sense,and the scope of the present invention is defined only by the appendedclaims.

In the following description, numerous specific details are set forth toprovide a thorough understanding of the invention. However, it isunderstood that the invention may be practiced without these specificdetails. In other instances, well-known structures and techniques knownto one of ordinary skill in the art have not been shown in detail inorder not to obscure the invention. Referring to the figures, it ispossible to see the various major elements constituting the apparatus ofthe present invention.

The proposed system, Delayed Telop Aid (DTA), improves theteleoperator's ability to control the vehicle in a three step process.First, DTA predicts robot motion given the operators commands. Second,DTA creates synthetic images to produce a video feed that looks as ifthe robot communication link had no delay and no reduced bandwidth.Finally, DTA performs closed loop control on the robot platform toensure that the robot follows the operator's commands.

This final step is one of the main characteristics that sets DTA apartfrom other approaches, a closed loop control of the platform makes surethat the predicted pose after the delay (and therefore the imagepresented to the operator) is achieved by the platform. This abstractsaway the latency-sensitive parts of the robot control, making therobot's behavior stable in the presence of poorly characterized latencybetween the operator and the vehicle. The work to perform these tasks issplit between two sets of computing hardware, one at the platform andthe second at the Operator Control Unit (OCU). At the platform theinventors have access to full resolution video at small predictablelatency, but in general the inventors lack the number-crunchingcapabilities to perform computationally expensive image reconstructiontasks. Also, at the time video is collected, the communication delayscan only be estimated. On the other hand, at the OCU the inventors couldeasily house the computational needed for image processing, but by thetime video arrives at the OCU it has been heavily processed to improvethroughput, and contains compression artifacts that make imageprocessing impractical.

The premise behind the inventors' Delayed Telop Aid (DTA) is that thepresent invention can extract sufficient information about the structureof the scene using image flow and other sensor specific techniques toreconstruct a useful predicted view of the world at a future point intime. This reconstruction can be used as a teleoperation aid. Thedesired result is to make it appear that the communication delay betweenthe OCU and the robotic platform does not exist. This eliminates thebiggest barrier to stable teleoperation, creating the illusion that theoperator is directly controlling the vehicle without the significantvideo and control delays. The feedback the operator receives from thevideo display would respond immediately to his actions, would not besubject to communications delays, and would accurately reflect thebehavior of the robot in the world.

The inventors developed various image reconstruction techniques tocreate the predicted synthetic video frames by using the delayed videoframe along with the associated vehicle status at that point in time andthe new commands queued up at the OCU. The inventors were able todemonstrate the first techniques on a live system utilizing a TALONrobot. The present invention's technique makes two important assumptionswhich, if perfectly met, eliminate the perceived delay:

1) The inventors can create a 3D model of the environment. This isneeded to be able to synthesize the image from the predicted pose.

2) The inventors can accurately control the vehicle to the predictedpose.

If both of these assumptions are satisfied, the operator should have theappearance of no delay, and the control will not be affected. It isclear that both assumptions have their issues. The inventors have beenable to control the vehicle with sufficient accuracy. Therefore, themain goal of the research was to evaluate techniques that could be usedto create the synthetic images from the 3D models. For this purpose, theinventors developed optical flow techniques such as SURF featuredetection and tracking, as well as the Lucas-Kanade method. Usingplatform navigation data for feature tracking improved the results. Theinventors also combined SURF feature tracking with Delaunay meshgeneration, thin plate spline interpolation and curve fitting forsynthetic image reconstruction. The data the inventors collected using aTALON platform allowed them to quickly evaluate different algorithms byplaying back collected data through the system.

Delaunay mesh generation and thin plate spline interpolation showedpromising results in creating these synthetic images. In addition to theabove techniques, the inventors also integrated a simple OpenGL basedimage reconstruction algorithm with our TALON platform. As part of thislive integration and testing exercise with the TALON, the inventorsdeveloped a new command interface to the TALON based on distance and yawcommands that would help guarantee that the platform follows the delayedcommands from the OCU accurately. This, in turn, helps in creatingaccurate synthetic future images for the operator.

Now referring to FIG. 1, the DTA Phase system 100 divides the requiredimage processing 101 between the vehicle 102 and the OCU 103, takinginto consideration the video quality and computational assets available.Images are taken by a camera 104 and processed 101 on the roboticplatform 102 along with navigational information 109 are sent to the OCU103 for reconstruction 105 via RF communication with input from thecommand history 106. Reconstructed images are sent to a video display107. An operator looking at the video display, can submit vehiclecontrols 108 with steering commands to a servo 110 located on theplatform 102 by RF communication. Vehicle controls 108 are also storedin the command history 106 as the controls occur and are used inreconstructing images 105.

The overall objective is to design a teleoperation aid that can be usedduring high latency teleoperation scenarios. The main goal of theresearch was to evaluate different techniques that could be used tocreate predicted synthetic video frames using predicted navigationstates of the platform, thereby giving the operator more confidence,control, and better situational awareness when teleoperating a roboticplatform using high latency communication links. The main tasksaccomplished are listed below:

The Inventor's developed the overall control paradigm, includingmessaging between OCU and robot as well as time tagging strategies. TheInventor's collected time tagged indoor and outdoor video and navigationdata using our development platform at company facilities. This allowedthe Inventors to experiment with different algorithms using the samedatasets thereby making the evaluation and comparison of differentalgorithms easier.

The Inventor's evaluated different feature detection algorithms such asSIFT, SURF, and Harris corner detectors. To improve feature tracking theinventors augmented their algorithms to use platform navigation data andtriangulated the feature locations, thereby increasing the accuracy ofour estimated 3D feature locations. In addition to using navigationdata, the inventors also applied Kalman filtering techniques to furtherrefine our estimate of feature locations in 3D space.

The Inventor's explored methods for generating predicted syntheticimages. Once the features were identified and tracked from one frame tothe next, the next step was to use these tracked features to createfuture synthetic video frames. Various image reconstruction techniquessuch as Delaunay mesh generation, thin plate spline interpolation, andcurve fitting were explored.

A simple OpenGL based simulated display was also explored and integratedwith our OCU. In this embodiment, by providing an algorithm to the OCUthat simulates the vehicle motion, given the operator commands to theinput device. The results of the simulator are displayed at the OCU tothe operator. The operator drives the vehicle from these simulatedresults, which are not subject to the communication delay. The resultsof the simulation are time-tagged and sent to the vehicle. A controlmechanism residing at the vehicle that controls the vehicle to followthe simulated positions created at the OCU using the algorithm.

The Inventor's integrated a simple OpenGL based display solution with areal platform. The inventors read queued commands from the joystick tosimulate platform movement in the OCU using OpenGL. The inventors alsodeveloped a new command interface on our platform to accept odometer andyaw commands instead of velocity and yaw-rate commands. This was done toclose the loop by making sure the platform followed the commandsprecisely thereby increasing the accuracy of the feedback shown to theoperator.

Now referring to FIGS. 2a, 2b, and 2c , a live teleoperation exerciseusing DTA with a TALON near the door. The operator in FIG. 2b isusing/holding the DTA widget 201 in the OCU to align the TALON to thedoor as show in FIG. 2a . FIG. 2c illustrates the OCU with platform icon202 and local obstacle map 203.

For the video data, the present invention collects raw Bayer images(1260×980) with GMT timestamps at 5 Hz. For the navigation data, thepresent invention collects the (x, y, z) location and (r, p, y)orientation of the platform along with GMT timestamps. The navigationdata was collected at 50 Hz. The camera location and orientation wasobtained by applying the appropriate coordinate transform to theplatform location. The combined location and orientation of the platformwill be referred to as “platform pose”.

FIG. 3 is a representation of the TALON test platform 300. The TALONtest platform has a Point Grey Chameleon camera 301 mounted along withan inertial measurement unit (IMU) 302.

The inventors experimented with different algorithms to extract featuresfrom raw images such as Scale Invariant Feature Transform (SIFT),Speeded Up Robust Features (SURF) and Harris corner detectors. Theinventors decided on using SURF because SURF provides scale- androtation-invariant interest point detectors and descriptors whilerequiring less computation that SIFT.

The standard version of SURF algorithm is several times faster thanScale-invariant feature transform (SIFT) and claimed by SURF's authorsto be more robust against different image transformations than SIFT.SURF is based on sums of approximated 2D Haar wavelet responses andmakes an efficient use of integral images. As basic image features theSURF algorithm uses a Haar wavelet approximation of the determinant ofHessian blob detector

The present invention's Tracking algorithm does the following: ExtractsSURF features for every frame; Projects SURF features from the imageinto the world frame using navigation data and our fish-eye cameramodel; Performs ray intersection checks in 3D for the ray originating atthe camera location and going through the feature location in worldframe. Each feature in the last frame is compared with every feature inthe current frame; If the rays are close enough, a 3D location for thatfeature is computed. (Condition 1). In addition to ray intersectionchecks, the algorithm also compares the similarity of the SURFdescriptors of the two features in question to increase the trackingaccuracy. (Condition 2). If both conditions are satisfied, the track isconsidered good and added as a node to be used during the imagereconstruction phase.

FIG. 4 illustrates SURF feature tracking between consecutive images. Thesolid/red lines 401 show the movement of the features from one image tothe next. The broken/green lines 402 are part of the Delaunay mesh withtracked features being the nodes in the mesh.

In order to increase the accuracy of the tracking algorithm, the presentinvention is augmented take advantage of the navigation data associatedwith each frame. The navigation data allows the algorithm to check formismatched SURF descriptors, and only allows pixels with reasonableshifts in the image to pass through. In the absence of this check, twovery similar SURF features at the opposite ends of the image could passthe filter and cause major distortions in the synthetic image createdduring the image reconstruction phase. FIG. 5 and FIG. 6 below show theresults. The image on the right hand side 501 in FIG. 5 looks muchbetter than the image 601 in FIG. 6. This is because the estimated 3Dlocation of the tracked feature is more accurate when using navigationdata which is evidenced by comparing the data trails of the left handside 501 of FIG. 5 illustrating image feature movement/trails comparedto the left hand side 602 of FIG. 6 which illustrates both theillustrating image feature movement/trails and navigation data.

Another improvement the inventors made to the tracking algorithm was touse a Kalman filter for each feature being tracked to improve ourestimate of its 3D location using its past history. The Kalman filter isinitialized with a best estimate of the 3D location of the feature usingthe method described above. At each iteration the filter is updatedusing the (x, y) location of the tracked pixel in the current image.Internally, the filter keeps track of the 3D state (x, y, z) of thefeature and updates the filter based on recorded measurements.

Delayed Telop Aid (DTA) 8 estimate of the 3D location of the featureusing the method described above. At each iteration the filter isupdated using the (x, y) location of the tracked pixel in the currentimage. Internally, the filter keeps track of the 3D state (x, y, z) ofthe feature and updates the filter based on recorded measurements.

The 3D locations of the tracked features can be sent to the OCU alongwith the compressed image and the associated navigation stateinformation of the platform. Here the 3D locations of the trackedfeatures are used to create a synthetic image using various imagereconstruction techniques.

There will be regions of the image where there are no discernablefeatures to track. In these regions it will not be possible to computean optical flow vector. The inventors deal with this by interpolating tothe surrounding flow vectors. The present invention does this bygenerating a triangular Delaunay mesh connecting the image features withknown optical flow vectors. The regions between are mapped ontotriangular regions. This surface, with the decompressed image from theplatform texture mapped to the image, will then be deformed to producethe reconstructed image for the operator to drive by. The predictedfuture locations of the tracked features are used to deform the Delaunaymesh.

As the operator steers, additional commands go into the history queueand to the vehicle itself. To simulate these joystick commands whenworking with recorded data, the present invention reads futurenavigation data from previously collected datasets. This produces a newfuture vehicle position estimate, and a new view position estimate foreach feature in the current image. The image is treated as a flexiblesheet or flexible two-dimensional plane with the locations of thetracked image features as the control points, which are inflexible. Whenthe image features are repositioned in the field of view based on thevehicle motion prediction, the portions of the image not directlyassociated with image features are interpolated based on the newpositions of the points in the triangular mesh. Using OpenGL, theinventors texture map the compressed image onto this warped surface.These images are then displayed on the screen. The black area 701 in thered circle 702 in the left image indicates that the platform will beturning to the left based on joystick commands from the operator asshown in FIG. 7.

Also notice the deformations on the door frame, which reflect thelimitations of using the Delauney mesh model. Next a comparison of asynthetic image (1 second into the future) that was created using thefollowing data as inputs: SURF features tracked between frames taken at5 Hz (0.2 s apart) and Platform pose 1 sec into the future from the timethe current frame that is being used to create the synthetic image wastaken (read from our collected dataset).

The present invention shows the future synthetic image 804 along withthe real frame 805 at the corresponding time side-by-side for yourevaluation below. Deformities introduced by the image reconstructionalgorithm are highlighted in the three circles 801-803 of FIG. 8. Thesynthetic image 804 was created using the Delaunay mesh algorithm asshown in FIG. 8. As can be seen, the synthetic image looks very real.

Thin-plate splines were introduced to geometric design by Duchon. Thename thin-plate spline (TPS) refers to a physical analogy involving thebending of a thin sheet of metal. In the physical setting, thedeflection is in the z direction, orthogonal to the plane. In order toapply this idea to the problem of coordinate transformation, oneinterprets the lifting of the plate as a displacement of the x or ycoordinates within the plane. According to Bookstein, TPS has an elegantalgebra expressing the dependence of the physical bending energy of athin metal plate on point constraints. For interpolation of a surfaceover a fixed set of nodes in the plane, the bending energy is aquadratic form in the heights assigned to the surface. TPS is anon-linear warp transform defined by a set of source and target featurelocations. A point on the mesh close to the source feature will be movedto a place close to the corresponding target feature location. Thepoints in between are interpolated smoothly using Bookstein's thin platespline algorithm.

In order to perform thin plate spline interpolation, the inventorsgenerate two lists consisting of the pixel locations of the trackedfeatures in the current image and the predicted future 2D pixellocations of the tracked features. The predicted future pixel locationsof the tracked features are computed by projecting the 3D featurelocation back into the synthetic 2D image using the world-to-imagetransformation provided by the camera model using the predictednavigation state of the platform.

Using the two lists above, the coefficients of an interpolated cubicfunction are computed. Once the parameters are estimated, new (x, y)locations of all the pixels in the synthetic future image are computed.This image is then texture mapped to a surface in OpenGL and displayedto the operator. The bending of the thin plate can be controlled using aRigidity parameter.

The thin plate spline algorithm improved the quality of the syntheticimages created using tracked SURF features. The thin plate splinealgorithm helped in reducing distortions caused by the triangles formedby features at different depths in the image when using Delayed TelopAid (DTA) and the Delaunay mesh, as highlighted by the red circle 901 inFIG. 9a . One of the drawbacks of the thin plate spline algorithm isthat the thin plate spline algorithm doesn't work well if there are veryfew features in an image.

The inventors also tried fitting a quadratic function to the depthvalues of the tracked features as shown in FIG. 10a 1001. The idea wasto draw the surface in OpenGL using the depths derived from this fittedfunction rather than drawing a flat wall at an arbitrary depth as in oursimple OpenGL based approach. The results were mixed since the fittedfunctions didn't represent correct depths for all surfaces in the image.The images shown in FIG. 10b show some results using this approach. Thex-axis on all the graphs below represent the pixels in the image(ranging from 0 to 639) and the depths are drawn on the y-axis. Thecompressed image 1002 is textured mapped to a surface that matches thecurve 1003 as shown in FIGS. 10a -b.

In order to test the entire system in a closed loop setup, the inventorsintegrated our image reconstruction modules with the OCU and our TALONplatform. The inventors performed the following tasks as part of thisintegration exercise.

A new command interface to control the TALON was developed. Normallytrack velocities are sent from the joystick to the TALON in order toteleoperate the command interface. Commanding the platform this way hassome drawbacks. It is hard to predict the future location of theplatform when using a velocity based command interface as velocity basedservoing is more error prone. A better way to command with a robot is tosend the robot desired odometer and yaw commands from the joystick. Thisallows the Inventors to better predict the future location of theplatform using the new commands thereby improving the accuracy of thesynthetic future images shown to the operator. This gives moreconfidence to the operator in terms of what the platform is actuallydoing and seeing.

New code was added to create odometer and yaw commands in the pendantprocess that interfaces with the joystick and send odometer and yawcommands to the platform. Code was added on the platform to readodometer and yaw commands and create odometer and yaw driving commandsfor the platform to follow.

The inventors also implemented a new process that runs on the platformthat associates the video frames taken each cycle with the correspondingvehicle pose at the time the frame was captured. The frame ID of theassociated camera frame is stored with the corresponding vehicle statusmessage and sent back to the OCU.

On the OCU side, the video frames taken each cycle associated with thecorresponding vehicle pose at the time the frame was captured are usedto correct the estimate of where the platform actually is based on whenand where the camera frame was captured. This correction in ourpredicted platform pose is then applied to adjust the syntheticpredicted frame currently being displayed to the operator.

As mentioned earlier, the inventors integrated a simple OpenGL basedimage reconstruction module in the OCU. The OCU takes the last videoframe received from the platform and texture maps the last video frameon a flat surface. The inventors then use the predicted pose of theplatform (computed using the commands sent to the platform from the OCU)to change the way the inventors look at this flat surface model. Theinventors use orthographic projection for this purpose.

OpenGL takes care of the rest and the inventors display this syntheticimage to the operator. The zoom in/zoom out effect is achieved bychanging the view port in OpenGL.

The odometer and yaw commands sent to the platform using the joystickare queued up in the OCU along with a timestamp. The imagereconstruction module has direct access to this queue of commands anduses the commands right away to provide instant feedback to the operatorby creating predicted synthetic frames based on those commands and thelast video frame/vehicle status received from the platform. The videoframes and vehicle status messages received from the platform are alsoqueued up in the OCU. In order to simulate latency, the camera framesare sent to the image reconstruction module after a predefined timeinterval has passed (from the time camera frames are taken). This delaythreshold is configurable to simulate different latencies. When theimage reconstruction module receives a new frame, the inventors find thecorresponding vehicle status message by matching the frame id stored inthe vehicle status message.

In another embodiment, by providing an algorithm to the OCU thatsimulates the vehicle motion, given the operator commands to the inputdevice. The results of the simulator are displayed at the OCU to theoperator. The operator drives the vehicle from these simulated results,which are not subject to the communication delay. The results of thesimulation are time-tagged and sent to the vehicle. A control mechanismresiding at the vehicle that controls the vehicle to follow thesimulated positions created at the OCU using the algorithm.

Let's say the delay was set to one second. The vehicle status receivedfrom one second ago is now used to correct our current estimatedplatform pose. This correction in pose is used when the inventorsgenerate the next synthetic image for the operator.

Live delayed compressed video from the SUGV Predicted synthetic videoTrack reference lines are drawn to help the operator TimestampedOdometer/Yaw commands, Timestamped Odometer/Yaw commands, andTimestamped JPEG video frames/Vehicle Status.

The present invention can be made into a “kit” of hardware and softwarecomponents that supports various sensors and different modes ofoperation. This kit is a software product for potential customers.

Basic Teleoperation mode: This is the base system that includes theSensor Data Capture and Processing Library and the Vehicle ControlLibrary runs on the platform and the Image Reconstruction Library andthe Vehicle Command Library that can be integrated with any OCU. The kitincludes an OCU integrated with DTA out of the box. The Sensor DataCapture and Processing Library provides support for common-off the-shelfsensors. The Image Reconstruction Library includes all the algorithmsfor creating synthetic images to help improve the operator's capabilityto teleoperate the vehicle by eliminating the effects of latencies inboth directions.

Teleoperation mode with Reactive Obstacle Avoidance: This packageprovides the platform the ability to detect obstacles and stop in thecase where blindly following commands from the operator results in acollision with an obstacle that is sensed by the platform. Thisfunctionality provided by the World Modeling and Path Planning andObstacle Avoidance libraries provided in this kit. The user will not beable to use the path planning features in the library in this mode.

Teleoperation mode with Path Planning and Obstacle Avoidance: Whenrunning in this mode, the platform detects and avoids obstacles detectedby the platform. The platform It corrects for erroneous commands sentfrom the OCU due to over steering. The platform uses the operatorcommands as reference for path following but also evaluates other pathsand provide feedback to the operator.

The present invention also has a sensor system designed and implementedon a military-relevant small robotic platform as shown in FIG. 11a . Theinventors propose to provide this 360 degree video and 360 degree LADARimaging and required computing and communications hardware as optionalcomponents of the Teleoperation Kit. The potential customer can chooseto buy this hardware or integrate our software libraries with theirhardware. The hardware components of the kit can be chosen based oncustomer requirements.

The RASRBot sensor head 1101 illustrated in FIG. 11a includes 360 degreevideo and LADAR. Three fisheye cameras are fused together to form a360×100 degree image around the robot. The LADAR is a 270 degree singleline scanner. A mirror 1102 reflects the normally vertical looking 90deg to a horizontal scan. A controllable pan motor rotates the LADARproviding vertical scans for terrain slope detection and a horizontalscan for thin obstacle detection and registration. In FIG. 11 b, 3Dscans of an office space are shown.

The inventors continue the development and tailoring of various imagereconstruction approaches to create better synthetic images that portraythe current state of the environment as seen by the platform. Throughtesting and research, the inventors have continually added functionalityto the platform to control the vehicle based on odometer and yawcommands as development has progressed. Based on live testing results onthe TALON, the inventors were satisfied with command followingcapability of the platform. The platform followed the commands veryaccurately, making the prediction part easier. Given those results, thehighest priority steps are improving synthetic image generation, fastaccurate world modeling and supporting varying levels of autonomy.

The Image Reconstruction Module that runs as part of the OperatorControl Unit (OCU) will be completely independent of the sensors on theplatform.

As shown in FIG. 12, the Sensor Data Capture and Processing Module 1202on the platform extracts 3D feature locations from the sensor 1201selected by the user/operator. These tracked 3D features along with thecurrent navigation state of the platform and a compressed image is sentback to the OCU.

The Sensor Data Capture and Processing Module 1202 runs on the platform.Each sensor 1201 that is added to the platform 1203 has its own sensordata processing class that communicates with the sensor 1201 bandextract the feature information from that sensor. These sensor dataprocessing classes support a generic sensor interface that is used bythe rest of the system to access and distribute sensor data. Thisinterface allows users to seamlessly swap sensors as well as add newsensors 1201 to the platform 1203 in the future and use them with DTA.

The image reconstruction module 1204 is a collection of algorithms thatcould be selected by the user to reconstruct synthetic images from the3D feature locations obtained from the platform 1203. The inputs comingin from the platform 1203 for each algorithm is a set of 3D featurelocations extracted from the sensor data, the navigation state of theplatform when the data was collected, a compressed image from the cameraat that moment in time and the last command that was executed on theplatform. The inputs from the OCU itself are the current commands beingsent to the platform from the Vehicle Command Module 1205. The output ofeach image reconstruction algorithm is a synthetic image that portraysthe predicted state of the platform for each command sent to theplatform.

A Vehicle Command Module 1206 provides an interface to send commandsfrom joysticks or other devices to the Image Reconstruction Module 1204as well as to the Vehicle Control Module 1205.

A Platform Pose Prediction sub module 1207 evaluates the commands sentto the platform on model of the platform and the world around theplatform. The Platform Pose Prediction sub module estimates the newposition of the platform given a command by taking into account theenvironment and the path following capabilities of the platform as wellas the current system mode. This estimated position can be used by theImage Reconstruction module 1204 when creating the synthetic imagesshown to the operator instead of directly using the commands from thecontroller.

A Vehicle Control Module 1205 resides on the platform and can be used totalk to the platform's drive by wire interface. The Vehicle ControlModule receives commands from the Vehicle Control Module 1250 on theOCU. The Vehicle Control Module sends back status to the Navigation DataInterface 1208.

A Navigation Data Interface 1208 provides system navigation stateinformation to other modules running on the platform. A World ModelingModule 1210 receives data from the Sensor Data Capture and ProcessingModule 1202 as well as the Navigation Data Interface 1208. TheNavigation Data Interface creates a 2.5D obstacle map of the environmentaround the platform. This map is used for Path Planning and ObstacleAvoidance and Path Planning Module with Obstacle Avoidance 1209. Thismodule uses commands sent from the operator as a reference and evaluateother paths that might be better to get to a certain result and provideappropriate feedback to the operator. The operator can use this feedbackto come up with a better route to tele-operate the vehicle. The PathPlanning and Obstacle Avoidance and Path Planning Module with ObstacleAvoidance also has the capability to perform reactive obstacleavoidance.

The system provides buttons and sliders in the OCU that allows theoperator to pick the sensor, system mode, and image reconstructionalgorithm as well as control the amount of sensor data processing thatis done on the platform. The OCU communicates with all other modules inthe system both on the OCU and the platform.

The inventors refer to the process of creating new images based oncurrent commands from delayed sensor data as synthetic image generation.The inventors have explored multiple Structure From Motion (SFM) basedapproaches as discussed above. The inventors plan to continue refiningthe promising approaches as well as evaluate some new modalities usingdifferent types of sensors.

It is possible to extract depth information from the sequences ofimagery from a single camera. When performing sparse featurelocalization, only highly unique image patches were triangulated acrossimages. The inventors continue to explore ways of increasing the densityand accuracy of the generated 3D models.

The inventors started by generating Delaunay meshes from the estimatedfeature locations but the resulting images did not look smooth. Theinventors noticed spikes in these synthetic images when the mesh hadconnected points at different depths in the same triangle. Thisindicated a problem with the way the mesh was being created andstretched to produce the synthetic images. Hence the inventors decidedon evaluating thin plate splines as a solution to get rid of thoseunpleasing artifacts observed when using Delaunay meshes. As a less CPUintensive approach, the inventors also evaluated fitting one dimensionalsplines (curve fitting) to the depths of features passed to the OCU.This allowed the Inventors to draw a surface in OpenGL at depths basedon the estimated curve thereby increasing the accuracy of the syntheticimage. This would greatly increase the confidence and situationalawareness of the operator. The inventors plan on refining our imagereconstruction algorithms based on thin plate splines and curve fittingin Phase II and integrate them into our OCU.

As mentioned earlier, thin plate splines enable the Inventors to createsmooth transitions between synthetic images created using predicted 3Dfeature locations. Thin plate splines are used in a various fields forthe same reason. Thin plate splines are heavily used in biomedical imageprocessing to estimate the shape of the various body parts and organsfrom sparse data sets. The main concept is that the original image isallowed to be deformed so that the original landmarks are moved to fitthe new shape. Thin plate splines are a class of non-rigid splinemapping functions f (x, y) with several desirable properties for ourapplication. Thin plate splines are globally smooth, easily computableand separable into affine and non-affine components. The thin platespline is the two-dimensional analog of the cubic spline in onedimension and contains the least possible non-affine warping componentto achieve the mapping. By the last statement, the inventors mean thatthe sum of squares of all second order partial derivatives i.e., thebending energy, is minimized. By using two separate thin plate splinefunctions fx and fy which model the displacement of the landmarks in thex and y direction the inventors arrive at a vector-valued functionF=(fx, fy) which maps each point of the image into a new point in theimage plane: (x, y)→(fx (x, y), fy (x, y)) [2]. This spline defines aglobal warping of space, and is therefore used to warp the entire sourceimage onto the target shape.

Arigovindan et al propose a novel method for image reconstruction fromnon-uniform samples with no constraints on their locations. They adopt avariational approach where the reconstruction is formulated as theminimizer of a cost that is a weighted sum of two terms: 1) the sum ofsquared errors at the specified points and 2) a quadratic function thatpenalizes the lack of smoothness. They search for a solution that is auniform spline and show how that solution can be determined by solving alarge, sparse system of linear equations.

Arigovindan et al propose a novel 2D method to synthesize face imagesacross pose from a single example. Starting from a training set ofsparse face meshes, they built a Point Distribution Model and identifythe eigenvectors which are responsible for controlling the apparentchanges in shape due to turning and nodding the head, namely the poseeigenvectors. By modifying the values of their corresponding parameters,virtual meshes under different poses can be obtained and, using thecorrespondences between the original mesh and the virtual one, syntheticfaces are generated via thin plate spline-based warping. Arigovindan etal also show that identity, as well as expressions, are not distorted inthese synthetic faces.

Texture mapping typically refers to the process of geometricallytransforming a given source image or pattern in order to simulate itsmapping onto a three-dimensional surface. There are potentially two waysto do this: 1) applying the inverse transformation for each pixelposition in the target image to get the interpolated value from thesource image; 2) applying the transformation of each source pixel andusing a nonuniform reconstruction method to get the target image. Thesecond method has the clear advantage that it uses the informationpresent in the source image completely, whereas there might be some lossof information with the first approach (unused pixels in the sourceimage). This method gives the least squares fit in the regions where theinput samples (transformed source pixels) outnumber the reconstructiongrid points (target pixels). This reduces reconstruction artifacts. Akey feature of this technique is that there are no aliasing artifactsand that the sharpness of the pictorial information is essentiallypreserved when is small.

Structured-light systems use triangulation to determine the structure ofthe environment in a very similar manner to traditional two-camera,passive stereo vision systems. A classic implementation of stereo visiontechnology consists of two calibrated cameras separated by a knowndistance viewing a common scene. To determine the structure of theenvironment, the pixel locations of common points in the scene are foundin both images. For example the corner of a desk may be located at pixellocation (30, 67) in the left camera and pixel location (30, 34) in theright camera. The difference in image locations is known as disparity ordisplacement. The difference in image locations is known as parallax andcan be used to determine the location of image points relative to thecamera system. One key thing to note about stereo vision systems is thatthe system relies on matching small image patches between the twocameras. The correspondence of pixels becomes very difficult inenvironments with uniform patches and smooth gradients (e.g., paintedflat walls).

Stereo vision was intended to solve some of the cost issues associatedwith LADAR, but stereo vision adds new quirks of its own. Typically,stereo is not as accurate as LADAR because stereo does not take directmeasurements of range.

Rather, stereo computes range based on associating pixels between twocameras and computing position based on triangulation. Pixelassociations are found using features in the environment that areobserved by both cameras.

Incorrect feature matches lead to erroneous depth estimates. Also, thedistinguishable range resolution is constant for LADAR sensors, butdegrades with the square of the distance from the cameras for stereovision.

MICROSOFT's KINECT sensor does not require finding features in theenvironment for stereo processing; the KINECT avoids finding features bygenerating its own features (a process known as structured light) thatare then used to triangulate and compute range. Because of thisadvantage, the KINECT shines in areas where conventional stereo visionhas problems: featureless walls and the repeating patterns created byvegetation or paved roads.

The fundamental difference between structured light and stereo vision isthat the second camera is replaced with a small light projector. A lightpattern is projected onto the environment and sensed by a camera. Thelight pattern is known a priori and is detected in the camera's image.The camera/projector parallax causes distortion of the light pattern inthe sensed image. This distortion provides a disparity estimate that canbe used to triangulate points in the scene. The key advantage ofstructured-light over stereo vision is that disparity can be measured inscenes with little image texture. In addition, structured-light istypically not affected by low-light conditions since structured-lightprovides its own light source (the projected light pattern). However,structured-light can exhibit problems if the ambient light is muchstronger than the projected light. Although KINECT doesn't workoutdoors, other structured light ranging approaches can also be devisedfor outdoor purposes for larger vehicles.

FIGS. 13a-c show two approaches to displaying the 3D environment. Aspart of the work for UMAPS, the inventors quickly discovered that theoptimal display approach depends on the requirements of the user. If 3Dsurface detail is desired, the representation shown in FIG. 17a is idealbecause 3D surface detail clearly shows all of the detail for eachobject in the environment. The textured surface on the right of FIG. 17ais ideal for enhancing the user's understanding of the environment.

It is important to note that the 3D mesh shown by the view in FIG. 13ais efficiently stored. All 3D point information is transformed into 3Dtriangles. Coplanar points are merged into similar surfaces. Thus, thisrepresentation creates a very compressed version of the 3D information(about a 20 to 1 compression ratio). This information is easy to storecompared to raw point clouds and has the added benefit of being easy tocompute.

Because of the compression, it is possible to wirelessly transmit these3D surfaces to the users in real time.

Figure s 13 a-c illustrate the 3D visualization of a single room. The 3Dsurfaces are generated from LADAR data. FIG. 13a illustrates a linkedview between non-textured 3D data and the same 3D surface textured withimage data shown in FIG. 13b . FIG. 13c illustrates a raw image of thescene captured using a fisheye lens. This image is used to texture theregion shown in FIG. 13a and create the image shown in FIG. 13 b.

The current UMAPS system utilizes the mixed set of visualizations shownin FIG. 17a , where camera position for the different displays is tiedtogether. That is, if the position moves for one view (e.g., the texturemapped display), then the others (e.g., the 3D model) are moved in thesame way. This approach has the benefit of maintaining 3D and textureinformation in an intuitive format and without needing to switch displaymodes. Moreover, the UMAPS visualization program allows the efficientdisplay of very large scenes without sacrificing performance. Theselarge scenes are shown by only drawing high resolution areas if thelarge scenes are close to the camera. For regions far from the camera,low resolution 3D information is drawn, which is much fewer trianglesthan the 3D surfaces. Moreover, the computations for what areas areclose to the camera are very efficient because a spatial data structureis utilized.

The inventors also plan on experimenting with a low-cost method forestimating scene depth using a camera and a laser pointer rig similar tothe one in the figure below. This setup is similar to the Kinect sensorwhere the inventors replace the structured light projector with cheaplaser pointers. By detecting the laser points from another camera, theinventors can compute depths of the laser pointer locations in thescene. Basically the inventors are spraying features in the environmentas the inventors go and tracking them.

Once the inventors have 3D locations of the laser points in the scene,the #d locations of the laser points in the scene are passed to the OCU.This information is used by the OpenGL based image reconstruction methodto texture map the compressed image to the surface drawn at the depthsassociated with these laser points. By increasing the number of laserpointers, the inventors can increase the accuracy of our syntheticimages. If the system has omni-directional cameras, the inventors canuse a circular laser rig to estimate the depth of points around theplatform. This might aid the operator when backing up the platform. Theinventors used the method described to detect the laser points in theimage.

The method consists of two main steps. The first is the computation of adifference image, the second the computation of a cross-correlationfunction. For the difference computation the inventors take a picture ofthe scene without laser points. Then, for the image containing laserpoints, the inventors compute the intensity difference in the red andgreen channels. Laser points in images usually do not appear as singlebright pixels but rather as circular or oval regions several pixels indiameter with the intensity maximum in the middle and intensity quicklydecreasing towards the boundary of the region. Since such an intensitydistribution resembles a two-dimensional Gaussian quite well, theinventors chose to detect laser points by computing thecross-correlation of a Gaussian and the difference image. The presentinvention then detects the positions of a set of local cross-correlationmaxima equal to the number of laser pointers and store them for furtherrefinement. A simple non-maximum suppression around each detected pointavoids false positives caused by nearby correlation maxima. Theinventors also used the method discussed in to achieve sub-pixelaccuracy.

A reactive scheme utilizes a sense-act coupling to navigate. Instead ofcreating a path or trajectory, a reactive navigation scheme createssteering and/or propulsion commands to react to a current snapshot ofsensor data.

Similar to a closed loop feedback system, the robot acts, changes theworld, and modifies the action in response to its current sensormeasurements. Predictions of the robot's future or knowledge of its pastare unused. The overall behavior is dictated by the series of commands(emergent behavior) rather than one single trajectory.

Since the sensing and acting is tightly coupled, the algorithm canoperate extremely quickly. For example, no computation is needed totransform map data to the earth frame and no memory is required to storethe map data from previous time steps. Instead, all sensor data, exceptfor navigation data (e.g. GPS), is egocentric. Behaviors that useexteroceptive sensory data typically do not require complex manipulationof the data to make decisions. The data can retain its reference to thevehicle frame. Reactive behaviors typically have low computationalcomplexity, often on the order of O(n).

The Vector Field Histogram (VFH+) algorithm was originally developed byJohann Borenstein at University of Michigan. In our system, VFH uses aone-dimensional ego-polar histogram, which is continuously updated everycycle by the SFM module. The ego-polar histogram is constructed with thepolar origin centered on the vehicle. The histogram contains an obstacledensity value at each corresponding angular direction. The desiredsteering direction is then determined by calculating the optimal angulardirection in the histogram.

SFM module outputs a two-dimensional image (C) with range estimate andconfidence value for each pixel. This data is then reduced into atwo-dimensional polar domain (P) as shown in FIG. 19. Each column in theimage domain is a one for one mapping to a wedge in the polar domain.The object density for each wedge of the polar domain is calculatedusing

${m_{\theta} = {\sum\limits_{i = 0}^{n}\lbrack {c_{i}^{2}( {1 - \frac{d_{i}}{d_{\max}}} )} \rbrack}},$where: mθ is object density at a specific angular position, i is theobject number, n is the number of objects, di is distance tocorresponding obstacle, dmax is maximum distance threshold, and ci isconfidence of corresponding obstacle.

This ego-polar histogram is an alternative to constructing a world mapin the global frame. The histogram data structure provides an efficientmeans for fusing the range data with the range confidence. The abilityto fuse confidence into the data structure allows the system to utilizecoarser obstacle estimates to navigate the robot.

Unlike a deliberative scheme, the system is capable of utilizing coarsermeasurements from the SFM module rather than requiring very dense,accurate range data from expensive, large range sensors, such as LADAR.This is a very hard problem due to very small subpixel motion betweenframes for these objects. The scene depth is estimated from the scalingof supervised image regions. The system then generates obstaclehypotheses from these depth estimates in image space.

A second step then performs testing of these by comparing with thecounter hypothesis of a free driveway. The approach can detect obstaclesalready at distances of 50 m and more with a standard focal length. Thisearly detection can allow the system to react in time to avoid thecollision. The inventors plan to investigate similar approaches foroutdoor high speed teleoperation even if the latency is pretty low. Thiskind of a safety system will increase the teleoperator's confidence inachieving mission success.

One possible mode for the enhanced teleoperation system would to use thecommands coming down from the OCU to the platform as a long range planand use the local planner on the platform to generate navigationcommands for the platform. The local planner is aware of the terrain andobstacles in its surroundings and can plan around these obstacles andhazardous terrain. The planner can also provide feedback to theoperator.

The operator will still have final control over the system but thesystem will be able to take certain decisions by itself.

The Path Planning and Obstacle Avoidance Module's function is togenerate trajectories for the UGV, avoiding obstacles while trying tofollow the commands generated from the OCU. The module resides on eachplatform. The path planner's input is a 3D representation of itsvicinity in a relative coordinate frame; the Path Planning and ObstacleAvoidance Module outputs a trajectory to be followed by the VehicleControl Module.

Thus, it is appreciated that the optimum dimensional relationships forthe parts of the invention, to include variation in size, materials,shape, form, function, and manner of operation, assembly and use, aredeemed readily apparent and obvious to one of ordinary skill in the art,and all equivalent relationships to those illustrated in the drawingsand described in the above description are intended to be encompassed bythe present invention.

Furthermore, other areas of art may benefit from this method andadjustments to the design are anticipated. Thus, the scope of theinvention should be determined by the appended claims and their legalequivalents, rather than by the examples given.

The invention claimed is:
 1. A method for teleoperation of a roboticvehicle, the method comprising: at a first time, using a camera of therobotic vehicle, detecting a first image of an environment in which therobotic vehicle is disposed; using a two-way communication medium,transmitting the first image from the robotic vehicle to an OperatorControl Unit (OCU), the transmission being received by the OCU after afirst delay; receiving, from an operator at the OCU, one or more firstcommands to move the robotic vehicle; simulating, based on the one ormore commands, motion of the robotic vehicle so as to predict a positionand pose of the robotic vehicle within the environment at a second timeafter the first time; displaying, based on the simulated motion and thetransmitted first image, a synthetic image to the operator, thesynthetic image including a projection of the robotic vehicle within theenvironment based on the predicted position and pose; using the two-waycommunication medium, transmitting the predicted position and pose fromthe OCU to the robotic vehicle, the transmission being received by therobotic vehicle after a second delay; and using a closed loop controlmechanism of the robotic vehicle, and based on the received transmissionfrom the OCU, controlling the robotic vehicle to substantially attainthe predicted position and pose at the second time.
 2. The method ofclaim 1, wherein the two-way communication medium is configured for datatransfer between the OCU and multiple robotic vehicles.
 3. The method ofclaim 1, wherein the two-way communication medium comprises radiotransmission.
 4. The method of claim 1, wherein a duration of the firstdelay is the same as that of the second delay.
 5. The method of claim 1,wherein a duration of the first delay is different from that of thesecond delay.
 6. The method of claim 1, comprising: using the camera ofthe robotic vehicle, detecting images at different times; tagging eachimage with a corresponding time of detection; using the two-waycommunication medium, transmitting the time-tagged images from therobotic vehicle to the OCU; and based on the transmitted time-taggedimages and the simulated motion, displaying to the operator a video feedthat includes the projection of the robotic vehicle.
 7. The method ofclaim 6, comprising: tagging, in each image of the video, the predictedposition and pose of the robotic vehicle with a corresponding time,wherein the transmitting the predicted position and pose includestransmitting the time-tags with the predicted position and poses to therobotic vehicle.
 8. The method of claim 1, wherein the receiving of theone or more first commands comprises receiving operator input via ajoystick, pad, or wheel of the OCU.
 9. The method of claim 1, whereinthe projection comprises an orthographic projection.
 10. The method ofclaim 1, wherein the one or more first commands comprise odometer andyaw commands.
 11. The method of claim 1, further comprising: at a thirdtime after the first time and before the second time, using the cameraof the robotic vehicle, detecting a second image of the environment;using the two-way communication medium, transmitting the second imagefrom the robotic vehicle to the OCU; adjusting the previously predictedposition and pose of the robotic vehicle based at least in part on thetransmitted second image; and further displaying a revised syntheticimage to the operator based on the adjusted predicted position and pose.12. The method of claim 1, wherein the displayed synthetic imagecomprises the first image texture mapped onto a flat surface.
 13. Themethod of claim 1, wherein the controlling the robotic vehicle using theclosed loop control mechanism is such that the robotic vehicleautomatically determines a path, from a current position and pose of therobotic vehicle to the prediction position and pose of the roboticvehicle at the second time, that avoids obstacles in the environmentsensed by the robotic vehicle.